The statements in this section merely provide background information related to the present disclosure and may not constitute prior art.
A challenge in present day manufacturing applications is a lack of capability to rapidly and accurately identify the source of manufacturing non-conformance instances. By “non-conformance” it is meant any situation, instance, or condition that deviates from nominal, and may include any situation, instance, or situation where a problem, fault or inconsistency is suspected to exist while a manufacturing operation is underway, and where such problem, fault or inconsistency may also negatively affect a life cycle of component, subsystem or product being produced by the manufacturing operation. Such a manufacturing operation could be, for example, the manufacture of a large commercial jet aircraft, and a specific manufacturing non-conformance instance could be the discovery of a manufacturing non-conformance, for example a broken electrical connector, at a specific stage of the manufacture of the aircraft.
Complex manufacturing systems typically involve the use of large scale databases and can present especially significant challenges in assisting a person in identifying the root cause (or root causes) of a discovered manufacturing non-conformance. Databases used in complex manufacturing applications often contain a large quantity of diverse information relating to system parts, non-conformance reports, operational histories, process notes and observations by a human observer, etc. Such diverse information can be quite extensive in size. This makes it difficult or impossible for a user to effectively and efficiently analyze all the information available in a manner that enables him/her to identify the root cause of a manufacturing non-conformance.
In addition, the present day database used in manufacturing applications often contain textual content that is input by numerous designers, producers, operators, technicians, maintenance personnel and other contributors. This gives rise to many differences in documentation approach, vernacular and spellings of words, phrases, etc. that describe a manufacturing non-conformance or a particular component parts. Thus, there exists a significant challenge to extract pertinent information from large volumes of current and historical free text, which leads to a multitude of correlation issues that add to the complexity of the manufacturing non-conformance (or inefficiency) analysis. This can also result in a plethora of computational and analytic problems. The usual result is long analysis mitigation times which lead to high costs and unacceptable delays in a manufacturing process, and which can be highly burdensome to an organization, or simply unacceptable for many businesses and governmental operations. Thus, with traditional analysis, the root cause of many manufacturing non-conformances may rarely be discovered because it simply takes too much time to thoroughly investigate a given manufacturing non-conformance. As a result, engineers or technicians may be forced to simply replace a component or modify an assembly process to ameliorate the immediate issue without ever determining the root cause of the non-conformance.
In addition to the above limitations, present day manufacturing quality monitoring or analysis systems typically do not allow for whole text capture, and are thus limited in their ability to relate specific types of information having to do with a given manufacturing non-conformance. By “whole text capture” it is meant using all the words that a user selects to describe a specific manufacturing issue. Furthermore, modern data mining solutions used in connection with present day relational databases are typically reductive and may not provide for the use of certain information (e.g., words or numbers) provided by the user in an initial manufacturing non-conformance query. These reductive solutions tend to lose the subtleties of the data that often are key in determining desirable patterns that do not repeat often. Such systems are often “forced” into characterizing specific information or attributes by pre-defined characteristics. For example, many database systems have drop-down menus that allow for only certain categories or to be chosen. The categories may not contain enough detail to adequately address all the associations between different types of information, and this may result in the system omitting relationships that can be of significant assistance in determining a desired result or that may assist in an analytical process. For example a relational database might force a particular non-conformance to be described as “connector did not work”. However, the true nature of the non-conformance might be most accurately described as “connector pin bent”, or “connector not tightened sufficiently”. Thus, the user may not be able to most accurately describe the manufacturing issue in his/her initial inquiry to the system, and this inability results in the system not having all of the useful non-conformance inquiry information that fully describes the issue.
Also, modern data mining solutions are time consuming and costly in terms of manpower hours. Such data mining systems are also typically processor intensive in their operation. This often results in long times for the data mining system to obtain the information that may be relevant to a lifecycle management system.